In a study to be published by Clinical Neurophysiology, and now posted online, engineering and health sciences researchers at McMaster University applied machine learning to EEG patterns and successfully predicted how patients with schizophrenia would respond to clozapine therapy.
Clozapine is recognized as an effective treatment for chronic medication-resistant schizophrenia but can produce serious side effects such as seizures, cardiac arrhythmias or bone marrow suppression. Some patients can develop blood problems that are life-threatening. Weekly to monthly blood sampling is required.
"Some people can suffer terrible side effects from clozapine," said Dr. Gary Hasey, associate professor at McMaster and director of the Transcranial Magnetic Stimulation laboratory at St. Joseph's Healthcare Mood Disorders Clinic in Hamilton. "The logistic difficulties for the patient and treatment team are also substantial. A method to reliably determine, before the onset of therapy, whether a patient will or will not respond to clozapine would greatly assist the clinician in determining whether the risks and logistic complexity of clozapine are outweighed by the potential benefits."
To conduct the study, EEGs were taken from 23 patients diagnosed with medication-resistant schizophrenia before they began taking clozapine. Twelve were men and 11 were women, all of middle age. The brainwave patterns and response to the clozapine therapy of these patients were used to "train" a computer algorithm to predict whether or not a specific patient will respond to the drug. The prediction accuracy was approximately 89 per cent. This algorithm showed similar predictive accuracy when it was further tested in a new group of 14 additional patients treated with clozapine.
This innovative work grows out of the close collaborative relationship between members of the Department of Electrical and Computer Engineering (Prof. James Reilly, Ph.D. student Ahmad Khodayari-Rostamabad), the School of Biomedical Engineering (Prof. Hubert de Bruin), and the Department of Psychiatry and Behavioural Neurosciences (Drs. Gary Hasey and Duncan MacCrimmon).
"The computational power available today supports new machine learning methodologies that can help doctors better diagnose and treat illness and disease," said Prof. Reilly. "Large amounts of data can be processed very quickly to identify patterns or predict outcomes. We're looking forward to applying the findings to other areas."
EEG records the brain's electrical activity close to the scalp. Traditionally, it has been used to monitor for epilepsy, and to diagnose coma, encephalopathies, and brain death. EEG is still often used as a first-line method to diagnose tumors, stroke and other focal brain disorders.
"EEG is an inexpensive, non-invasive technique widely available in smaller hospitals and in community laboratories," explains Dr. MacCrimmon. "Also, EEG readings take only 20 to 30 minutes of a patient's time, with no preparation required, so pose minimal inconvenience."
Funding for the research was provided in part by The Magstim Company Ltd., a developer and manufacturer of medical and research devices for the neurological and surgical fields. The company is based in Wales, U.K.
The researchers now plan to test their findings on a larger sample group. They have successfully demonstrated the application of machine learning methods for analyzing EEG signals to predict the response to various treatments available for patients with other psychiatric conditions, specifically major depression. They have also demonstrated the effectiveness of machine learning methods as a diagnostic tool for distinguishing various forms of psychiatric illness. It may also be possible to incorporate a range of other clinical and laboratory data such as personality inventory scores, personal and demographic information and treatment history to improve performance.
Gene Nakonechny | EurekAlert!
First form of therapy for childhood dementia CLN2 developed
25.04.2018 | Universitätsklinikum Hamburg-Eppendorf
Do microplastics harbour additional risks by colonization with harmful bacteria?
05.04.2018 | Leibniz-Institut für Ostseeforschung Warnemünde
Magnetic resonance imaging, or MRI, is a widely used medical tool for taking pictures of the insides of our body. One way to make MRI scans easier to read is...
At the Hannover Messe 2018, the Bundesanstalt für Materialforschung und-prüfung (BAM) will show how, in the future, astronauts could produce their own tools or spare parts in zero gravity using 3D printing. This will reduce, weight and transport costs for space missions. Visitors can experience the innovative additive manufacturing process live at the fair.
Powder-based additive manufacturing in zero gravity is the name of the project in which a component is produced by applying metallic powder layers and then...
Physicists at the Laboratory for Attosecond Physics, which is jointly run by Ludwig-Maximilians-Universität and the Max Planck Institute of Quantum Optics, have developed a high-power laser system that generates ultrashort pulses of light covering a large share of the mid-infrared spectrum. The researchers envisage a wide range of applications for the technology – in the early diagnosis of cancer, for instance.
Molecules are the building blocks of life. Like all other organisms, we are made of them. They control our biorhythm, and they can also reflect our state of...
University of Connecticut researchers have created a biodegradable composite made of silk fibers that can be used to repair broken load-bearing bones without the complications sometimes presented by other materials.
Repairing major load-bearing bones such as those in the leg can be a long and uncomfortable process.
Study published in the journal ACS Applied Materials & Interfaces is the outcome of an international effort that included teams from Dresden and Berlin in Germany, and the US.
Scientists at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) together with colleagues from the Helmholtz-Zentrum Berlin (HZB) and the University of Virginia...
13.04.2018 | Event News
12.04.2018 | Event News
09.04.2018 | Event News
26.04.2018 | Medical Engineering
26.04.2018 | Power and Electrical Engineering
26.04.2018 | Information Technology